{"id":"W2014291971","doi":"10.1287/inte.1090.0448","title":"Optimization Helps Shermag Gain Competitive Edge","year":2009,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Supply chain optimization; Procurement; Competitive advantage; Supply chain network; Software; Component (thermodynamics); Computer science; Market share; Total cost; Operations research; Supply chain management; Engineering; Business; Marketing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001923471,0.0001972035,0.0002101566,0.0002503829,0.0001296275,0.0001949932,0.0001676203,0.0001104672,0.0001649701],"category_scores_gemma":[0.00002319749,0.0001703009,0.00008290696,0.0003577324,0.00002304429,0.0001765583,0.000007851688,0.0004598385,0.0001216332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000135854,"about_ca_system_score_gemma":0.0000303176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.200942e-7,"about_ca_topic_score_gemma":1.935727e-7,"domain_scores_codex":[0.9989089,0.000005316783,0.0004197361,0.00009295045,0.0003004916,0.0002725744],"domain_scores_gemma":[0.999423,0.00003131975,0.0001002049,0.0001453408,0.0001091292,0.0001910316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001419009,0.0000239138,0.000005483867,0.000003657951,0.00003306729,0.000007771684,0.000143478,0.9814441,0.00002238961,0.005742795,0.0005208128,0.01203832],"study_design_scores_gemma":[0.0006584092,0.00007233719,0.00006394774,0.00003346763,0.0000186624,0.00003755229,0.000238461,0.995086,0.0006588679,0.000408061,0.002470839,0.0002534016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001326656,0.00005582746,0.8379253,0.0002586433,0.0004032675,0.0001188091,0.000005675091,0.0003086486,0.1595972],"genre_scores_gemma":[0.8159776,0.0008622718,0.1787536,0.003033661,0.0008484042,0.000003955516,0.00006737091,0.00006297381,0.0003900921],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.814651,"threshold_uncertainty_score":0.6944669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019653572668703,"score_gpt":0.2230742850534684,"score_spread":0.2128777493267814,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}